In Figure REF , we show additional qualitative comparisons between our method and the state-of-art approaches [1]}, [2]} on Stanford2D3d [3]}. In Figure REF , we show additional generalization capability comparisons between current state-of-the-art BiFuse (with fusion) and our PanoDepth, with training on 360D [1]} and testing on a different real-world Laval HDR dataset [5]} with no ground truth depth.
As can be seen from both figures, our method can more accurately recover the scene structure, predict the depth with less artifacts, and restore the object details, and has stronger generalization ability.
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